Date
2006Metadata
Show full item record
Preprint
Content aggregation on knowledge bases using graph clustering
Abstract
Recently, research projects such as PADLR and SWAP have
developed tools like Edutella or Bibster, which are targeted at establishing
peer-to-peer knowledge management (P2PKM) systems. In such a
system, it is necessary to obtain provide brief semantic descriptions of
peers, so that routing algorithms or matchmaking processes can make
decisions about which communities peers should belong to, or to which
peers a given query should be forwarded.
This paper proposes the use of graph clustering techniques on knowledge
bases for that purpose. Using this clustering, we can show that our
strategy requires up to 58% fewer queries than the baselines to yield full
recall in a bibliographic P2PKM scenario.
developed tools like Edutella or Bibster, which are targeted at establishing
peer-to-peer knowledge management (P2PKM) systems. In such a
system, it is necessary to obtain provide brief semantic descriptions of
peers, so that routing algorithms or matchmaking processes can make
decisions about which communities peers should belong to, or to which
peers a given query should be forwarded.
This paper proposes the use of graph clustering techniques on knowledge
bases for that purpose. Using this clustering, we can show that our
strategy requires up to 58% fewer queries than the baselines to yield full
recall in a bibliographic P2PKM scenario.
Citation
@article{urn:nbn:de:hebis:34-2009042027014,
author={Schmitz, Christoph and Hotho, Andreas and Jäschke, Robert and Stumme, Gerd},
title={Content aggregation on knowledge bases using graph clustering},
year={2006}
}
0500 Oax
0501 Text $btxt$2rdacontent
0502 Computermedien $bc$2rdacarrier
1100 2006$n2006
1500 1/eng
2050 ##0##urn:nbn:de:hebis:34-2009042027014
3000 Schmitz, Christoph
3010 Hotho, Andreas
3010 Jäschke, Robert
3010 Stumme, Gerd
4000 Content aggregation on knowledge bases using graph clustering / Schmitz, Christoph
4030
4060 Online-Ressource
4085 ##0##=u http://nbn-resolving.de/urn:nbn:de:hebis:34-2009042027014=x R
4204 \$dPreprint
4170
5550 {{Wissensextraktion}}
5550 {{Wissensmanagement}}
5550 {{Ontologie <Wissensverarbeitung>}}
7136 ##0##urn:nbn:de:hebis:34-2009042027014
<resource xsi:schemaLocation="http://datacite.org/schema/kernel-2.2 http://schema.datacite.org/meta/kernel-2.2/metadata.xsd"> 2009-04-20T09:35:48Z 2009-04-20T09:35:48Z 2006 urn:nbn:de:hebis:34-2009042027014 http://hdl.handle.net/123456789/2009042027014 252842 bytes application/pdf eng Urheberrechtlich geschützt https://rightsstatements.org/page/InC/1.0/ 004 Content aggregation on knowledge bases using graph clustering Preprint Recently, research projects such as PADLR and SWAP have developed tools like Edutella or Bibster, which are targeted at establishing peer-to-peer knowledge management (P2PKM) systems. In such a system, it is necessary to obtain provide brief semantic descriptions of peers, so that routing algorithms or matchmaking processes can make decisions about which communities peers should belong to, or to which peers a given query should be forwarded. This paper proposes the use of graph clustering techniques on knowledge bases for that purpose. Using this clustering, we can show that our strategy requires up to 58% fewer queries than the baselines to yield full recall in a bibliographic P2PKM scenario. open access Schmitz, Christoph Hotho, Andreas Jäschke, Robert Stumme, Gerd Auch erschienen in: Sure, York u.a. (Hrsg.): The semantic web. (Lecture notes in computer science ; 4011). Berlin u.a. : Springer, 2006. S. 530-544. ISBN 3-540-34544-2 = 978-3-540-34544-2 (The original publication is available at www.springerlink.com) Wissensextraktion Wissensmanagement Ontologie <Wissensverarbeitung> </resource>
The following license files are associated with this item:
:Urheberrechtlich geschützt